Multi-label Detection and Classification of Red Blood Cells in Microscopic Images

by   Wei Qiu, et al.

Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containing congregating cells) is more challenging, as separation of individual cells can be difficult (e.g. touching cells) or even impossible (e.g. overlapping cells). As multi-instance images are common in analyzing Red Blood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop and implement a multi-instance cell detection and classification framework to address this challenge. The framework firstly trains a region proposal model based on Region-based Convolutional Network (RCNN) to obtain bounding-boxes of regions potentially containing single or multiple cells from input microscopic images, which are extracted as image patches. High-level image features are then calculated from image patches through a pre-trained Convolutional Neural Network (CNN) with ResNet-50 structure. Using these image features inputs, six networks are then trained to make multi-label prediction of whether a given patch contains cells belonging to a specific cell type. As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples. Finally, for the purpose of SCD testing, we train another machine learning classifier to predict whether the given image patch contains abnormal cell type based on outputs from the six networks. Testing result of the proposed framework shows that it can achieve good performance in automatic cell detection and classification.


page 4

page 6

page 10

page 11


Topologically-Regularized Multiple Instance Learning for Red Blood Cell Disease Classification

Diagnosing rare anemia disorders using microscopic images is challenging...

Applying Faster R-CNN for Object Detection on Malaria Images

Deep learning based models have had great success in object detection, b...

Nuclei Detection Using Mixture Density Networks

Nuclei detection is an important task in the histology domain as it is a...

Accurate Tumor Tissue Region Detection with Accelerated Deep Convolutional Neural Networks

Manual annotation of pathology slides for cancer diagnosis is laborious ...

CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification

Automatic examination of thin-prep cytologic test (TCT) slides can assis...

Machine Learning Based Mobile Network Throughput Classification

Identifying mobile network problems in 4G cells is more challenging when...

Increasing a microscope's effective field of view via overlapped imaging and machine learning

This work demonstrates a multi-lens microscopic imaging system that over...

Please sign up or login with your details

Forgot password? Click here to reset